CN107482618A - Electricity pricing method with the generating of wind fire and the power network of flexible load containing polymorphic type - Google Patents
Electricity pricing method with the generating of wind fire and the power network of flexible load containing polymorphic type Download PDFInfo
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Abstract
The invention discloses a kind of electricity pricing method with the generating of wind fire and the power network of flexible load containing polymorphic type, this method comprises the steps of：Determine the fired power generating unit number of power network and the relevant parameter of every fired power generating unit, determine the sunrise force curve of wind-powered electricity generation, load is divided into four classes, it is determined that original electricity price and power consumption hourly per type load, establish the polymorphic type flexible load to be generated electricity with wind fire and coordinate Controlling model, utilize the above-mentioned model of differential evolution PSO Algorithm, the time-of-use tariffs strategy of four kinds of flexible loads of formulation.It is an advantage of the invention that：The demand response model of flexible load is established, the otherness according to different flexible loads to Respondence to the Price of Electric Power, formulates different electricity price incentive measures, fully excavates the demand response resource of all kinds of flexible loads, improves Operation of Electric Systems economy；There is preferable optimizing using DEPSO Algorithm for Solving, improve solving precision.
Description
Technical field
The present invention relates to flexible load Coordinated Control field, relates more specifically to consider there is wind-fire to generate electricity and contain
The electricity pricing method of polymorphic type flexible load power network.
Background technology
With the fast development of society and economy, the mankind are increasing to the demand of electric energy, and this also increases people couple
The demand of fossil energy.But traditional fossil energy can proved reserves it is limited, will be depleted after death；Except this
Outside, fossil energy pollution environment, seriously endanger health.To tackle above mentioned problem, China in Recent Years greatly develops wind-force
Generate electricity, but continuing to increase with installed capacity of wind-driven power, its fluctuation having and the intermittent economical operation for giving power system
Bring huge challenge.
To improve the Operation of Electric Systems economy containing wind-power electricity generation, more by formulating rational electrovalence policy, coordinate to use
Family load ordered electric, and it is grid-connected to solve large-scale wind power it is coordinated jointly with traditional fired power generating unit, improves power system
The economy of operation.Document (Zeng Dan, Yao Jianguo, Yang Shengchun, waits the price type based on security constraint in reply wind electricity digestions to need
Ask response optimization scheduling modeling [J] Proceedings of the CSEEs, 2014,34 (31):5571-5578.) consider in dispatching of power netwoks
Network Security Constraints and the constraint of demand response satisfaction, establish flexible load Real-Time Scheduling model, ensure power network in Real-Time Scheduling
Safe operation, but compared with implementing before Spot Price, the Spot Price strategy that the document is formulated may cause the user side electricity charge
Increase, it is impossible to ensure the economy of Operation of Electric Systems；Patent (meter and the Peak-valley TOU power price of system reliability and Trading risk
Determine method [P] Chinese patents:201410376427.1,2014-08-01) establish meter and system reliability and yield risk
Peak-valley TOU power price model, but the model only considered a kind of load when formulating Peak-valley TOU power price, not count and load
Diversity, in fact, the response characteristic of different load is not quite similar；Document (Li Yang, Wang Zhihua, Lu Yi, waits peak and valley times
The implementation of electricity price and the response of big industrial user [J] Automation of Electric Systems, 2001,25 (8):45-48.) one kind is proposed to contain
The TOU Power Price Model for thering is user to analyze tou power price degree of reaction, the peak interval of time division optimized and its corresponding
Tou power price pricing method, but do not account for user satisfaction.(Li Hui, Kang Chongqing, the summer, clear considered user satisfaction to document
Dsm price policy model [J] electric power network techniques, 2004,28 (23):1-6.) establish the DSM for considering user satisfaction
Price policy model, but fail to consider the change of demand charge expenditure.
In summary, it is existing flexible load to be participated in the research of electric power system dispatching, do not account for different flexible negative
Lotus is to the otherness of Respondence to the Price of Electric Power ability, and in actual electric network, industry, business and resident load to the responding ability of electricity price simultaneously
Differ, it is necessary to separately consider, different electricity price measures is formulated for it, fully excavates all kinds of flexible load resources.The present invention examines
Consider the otherness of industry, business and resident load to the responding ability of electricity price, different time-of-use tariffs are formulated for each type load, and
Influence to user's normal electricity consumption is reduced by meter and user satisfaction constraint, establishing there is wind-fire to generate electricity and to contain polymorphic type soft
Property load power network time-of-use tariffs model, form thermal power generation and polymorphic type flexible load and coordinate and optimize cooperation wind-electricity integration jointly
New model, Operation of Electric Systems economy is improved with this.
To obtain that there is the electricity price of wind-fire generating and the power network of flexible load containing polymorphic type, it is necessary first to determine some thermoelectricitys
The parameter of unit.Wherein, ai、biAnd ciIt is i-th fired power generating unit greenhouse gas, it is relevant with the inherent characteristic of unit, three
Parameter describes the fuel cost of fired power generating unit jointly.
Secondly, respondent behavior of the present invention based on Price elasticity matrix description load to electricity price.Price elasticity can react
The relation that electrical demand changes between electricity price change, Price elasticity matrix include self-elasticity coefficient and coefficient of cross elasticity,
It is defined as follows：
In formula：εiiIt is self-elasticity coefficient；εijFor coefficient of cross elasticity；LiWith Δ LiRespectively represent period i power consumption and
Variable quantity；pi、pjWith Δ pi、ΔpjPeriod i, j electricity price and the variable quantity of electricity price are represented respectively.
By defined above, following price elasticity matrix of demand is established：
In formula, hop count when n is.
Secondly, the present invention is flexible to being generated electricity with wind-fire and containing polymorphic type using differential evolution population (DEPSO) algorithm
The time-of-use tariffs model of load power network is solved.DEPSO algorithms proposed (Hendtlass from 2001 by Hendtlass.T
T.A combined swarm differential evolution algorithm for
Optimizationproblems.Lecture Notes in Computer Science, 2001,2070:11-18.), the calculation
Method is by population (PSO) algorithm and differential evolution (DE) Algorithm constitution.PSO algorithms are made up of many particles, and each particle is just
Equivalent to a bird in flock of birds, each particle image birds are the same to find optimum point in a manner of flight in an n-dimensional space, is flying
During row (iteration), information transmission is constantly carried out between particle, and learn each other.Particle has two spies in position and speed
Sign amount, the position of particle is a solution of required problem.Each particle can be stored after iteration each time from
The optimal solution obtained in searching process (is referred to as individual optimal solution pi), and learn all particle institutes by way of information transmission
Obtained optimal solution (is referred to as globally optimal solution pg), in each iteration, the speed v of particle i jth dimensionijWith position xijBy following
Expression formula renewal：
In formula, xi(t)=[xi1(t),xi2(t),...,xiN(t)]TAnd vi(t)=[vi1(t),vi2(t),...,viN(t)
]TRepresent respectively in N-dimensional space i-th particle (i=1,2 ..., PS) position vector and velocity in the t times iteration,
PSRepresent number of particles；pi(t)=[pi1(t),pi2(t),...piN(t)]TI-th of particle in N-dimensional space is represented to change at first t times
The particle personal best particle that generation is searched out；pg(t)=[pg1(t),pg2(t),...pgN(t)]TRepresent in N-dimensional space and own
Particle is in the global optimum position that preceding t iteration is searched out；r1jAnd r (t)2j(t) be two be distributed between (0,1) with
Machine number；W is inertia weight；c1And c2Referred to as acceleration parameter.
DE algorithms include variation, intersection and selection operation.For i-th DE individual mutation operation (i=1,2 ...,
PS), we randomly select three different individuals from population firstWithWillMake
, will for base vectorWithMake the difference and carry out after certain scaling withSuperposition, i.e.,：
In formula,For variation vector；F is zoom factor.
The method that crossover operation is intersected using binary, this method act onWith the population of last iterationMost
Produce eventually and sound out vectorI.e.：
In formula,WithVector is represented respectivelyWithJth dimension component, CRIt is crossover probability.
Selection operation is selected in filial generation and parent, if the desired value of filial generation vector is less than the target of parent vector
Value, then retain filial generation vector, eliminate parent vector；Otherwise retain parent vector, eliminate filial generation vector.
DEPSO algorithms are utilized respectively PSO algorithms and DE algorithms pair by the way of PSO algorithms and DE algorithm parallel computations
Particle position in population is updated, and after renewal terminates each time, asks for PSO optimal solution F respectivelyPSOIt is optimal with DE
Solve FDE, compare FPSOAnd FDE, choose optimal solution of the solution more excellent in both as population.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the present invention proposes a kind of generated electricity with wind-fire and containing more
The electricity pricing method of type flexible load power network, its object is to fully excavate the demand response potentiality of each type load, thus
Solves the technical problem of Economical Operation of Power Systems.
In order to achieve the above object, the present invention adopts the following technical scheme that.
A kind of electricity pricing method with wind-fire generating and the power network of flexible load containing polymorphic type, this method includes following
Step：
A, the fired power generating unit number N of power network, and the parameter of i-th unit are determined：I-th fired power generating unit cost of electricity-generating characteristic
Coefficient is ai、biAnd ci, the thermal starting of i-th unit and cold start-up expense are respectively Shot,iAnd Scold,i, the minimum of i-th unit
Available machine time and minimum downtime are respectively Tminrun,iAnd Tminstop,i, the cold start-up time T of i-th unitcold,i, i-th
The minimum output power P of thermal motormin,iWith peak power output Pmax,i, the descending climbing rate DR of i-th thermal motoriClimbed with upward slope
Ratio of slope URi, wherein i=1,2 ..., N；
B, the sunrise force curve of wind-powered electricity generation is determined；
C, load is divided into the P of industrial load oneload,1, the P of industrial load twoload,2, Commercial Load Pload,3And resident load
Pload,4Four classes, determine the original electricity price p of jth type load0, j, it is determined that under original electricity price state jth type load t hours use
Electricity L0(t),j, wherein j=1,2,3,4, t=1,2 ..., 24；
D, using Price elasticity matrix, model of the user to Respondence to the Price of Electric Power behavior is established；
E, with the minimum object function of fired power generating unit cost of electricity-generating, and power-balance constraint, fired power generating unit output work are provided
Rate constraint, the constraint of fired power generating unit climbing rate, the minimum operation of fired power generating unit and idle time constraint, the constraint of fired power generating unit spare capacity
Constrained with user satisfaction, establish the polymorphic type flexible load to be generated electricity with wind-fire and coordinate Controlling model；
F, using the above-mentioned model of differential evolution PSO Algorithm, the time-of-use tariffs of four kinds of flexible loads are obtained.
2nd, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the user described in step d is to the model of Respondence to the Price of Electric Power behavior：
In formula, p1(t),jThe electricity price reformulated for jth type load t hours, L1(t),jFor p1(t),jThe lower jth class of excitation is born
The power consumption of lotus t hours, t=1,2 ..., 24, j=1,2,3,4, wherein E is Price elasticity matrix：
3rd, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the foundation described in step e coordinates Controlling model with the polymorphic type flexible load that wind-fire generates electricity：
Object function
f(PG,i,t)=ai·(PG,i,t)2+bi·PG,i,t+ci
In formula, ui,tIt is i-th fired power generating unit in period t start and stop state, f (PG,i,t) for i-th fired power generating unit fuel into
This, PG,i,tIt is i-th fired power generating unit in period t average output, SiFor the start-up cost of i-th fired power generating unit, Toff,i,tFor
The when hop count that i-th fired power generating unit is continuously shut down in period t；
Constraints
1) power-balance condition
In formula, PW,jContributed for period t Wind turbines；
2) fired power generating unit power output constrains
Pmin,i≤PG,i,t≤Pmax,i
3) fired power generating unit climbing rate constrains
-DRi≤PG,i,t-PG,i,t-1≤URi
4) the minimum operation of fired power generating unit and idle time constraint
In formula, Ton,i,tThe when hop count continuously opened for i-th fired power generating unit in period t；
5) spare capacity constrains
Spare capacity constraint is divided into positive rotation Reserve Constraint and negative rotation turns Reserve Constraint, and wherein positive rotation Reserve Constraint is wind
Electricity causes required positive rotation standby when contributing minimum：
In formula, L% is the spinning reserve rate for system loading,PW,t For the lower limit of t period wind power outputs；
Negative rotation turns to cause required negative spinning reserve when Reserve Constraint is wind power output maximum：
In formula,For the upper limit of t period wind power outputs；
6) user satisfaction constrains
The satisfaction of jth type load power mode is ηj
In formula, ηj,minFor the minimum value of the satisfaction of jth type load power mode.
4th, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the model solution method described in step f：
The polymorphic type flexible load to be generated electricity with wind-fire is coordinated into Controlling model and is converted into a dual-layer optimization problem, outside
Layer optimization problem be four type load time-of-use tariffs determination, internal layer optimization problem for fired power generating unit start and stop state determination and
Start the output situation of unit；The time-of-use tariffs of the type load of random initializtion four, and ask on this basis under the time-of-use tariffs
The power consumption of four type loads each period, the power consumption of superposition four type loads each period obtain the electricity consumption of total load each period
Amount, then solved using the output of start and stop state of the differential evolution particle cluster algorithm to fired power generating unit and startup unit,
Optimal cost of electricity-generating in the case of the time-of-use tariffs is obtained, recycles differential evolution particle cluster algorithm repeatedly to the peak of four type loads
Paddy electricity valency carries out optimizing, takes the minimum four corresponding type load time-of-use tariffs of cost of electricity-generating under all situations as final electricity price
Scheme.
In general, by the contemplated above technical scheme of the present invention compared with prior art, have below beneficial to effect
Fruit：
1st, the demand response model of flexible load, and the otherness according to different flexible loads to Respondence to the Price of Electric Power are established, and
Different electricity price incentive measures is formulated for it, can fully excavate the demand response resource of all kinds of flexible loads, improves power system
Performance driving economy.
2nd, the polymorphic type flexible load that using DEPSO Algorithm for Solving there is wind-fire to generate electricity coordinates Controlling model, algorithm tool
There is preferable optimizing performance, improve solving precision.
Brief description of the drawings
Fig. 1 is the load curve schematic diagram under three kinds of electricity price pricing models of the embodiment of the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below
Conflict can is not formed each other to be mutually combined.
Generated electricity with wind-fire and the electricity pricing method of the power network of flexible load containing polymorphic type, this method comprise the steps of：
A, the fired power generating unit number N of power network, and the parameter of i-th unit are determined：I-th fired power generating unit cost of electricity-generating characteristic
Coefficient is ai、biAnd ci, the thermal starting of i-th unit and cold start-up expense are respectively Shot,iAnd Scold,i, the minimum of i-th unit
Available machine time and minimum downtime are respectively Tminrun,iAnd Tminstop,i, the cold start-up time T of i-th unitcold,i, i-th
The minimum output power P of thermal motormin,iWith peak power output Pmax,i, the descending climbing rate DR of i-th thermal motoriClimbed with upward slope
Ratio of slope URi, wherein i=1,2 ..., N；
B, the sunrise force curve of wind-powered electricity generation is determined；
C, load is divided into the P of industrial load oneload,1, the P of industrial load twoload,2, Commercial Load Pload,3And resident load
Pload,4Four classes, determine the original electricity price p of jth type load0, j, it is determined that under original electricity price state jth type load t hours use
Electricity L0(t),j, wherein j=1,2,3,4, t=1,2 ..., 24；
D, using Price elasticity matrix, model of the user to Respondence to the Price of Electric Power behavior is established；
E, with the minimum object function of fired power generating unit cost of electricity-generating, and power-balance constraint, fired power generating unit output work are provided
Rate constraint, the constraint of fired power generating unit climbing rate, the minimum operation of fired power generating unit and idle time constraint, the constraint of fired power generating unit spare capacity
Constrained with user satisfaction, establish the polymorphic type flexible load to be generated electricity with wind-fire and coordinate Controlling model；
F, using the above-mentioned model of differential evolution PSO Algorithm, the time-of-use tariffs of four kinds of flexible loads are obtained.
2nd, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the user described in step d is to the model of Respondence to the Price of Electric Power behavior：
In formula, p1(t),jThe electricity price reformulated for jth type load t hours, L1(t),jFor p1(t),jThe lower jth class of excitation is born
The power consumption of lotus t hours, t=1,2 ..., 24, j=1,2,3,4, wherein E is Price elasticity matrix：
3rd, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the foundation described in step e coordinates Controlling model with the polymorphic type flexible load that wind-fire generates electricity：
Object function
f(PG,i,t)=ai·(PG,i,t)2+bi·PG,i,t+ci
In formula, ui,tIt is i-th fired power generating unit in period t start and stop state, f (PG,i,t) for i-th fired power generating unit fuel into
This, PG,i,tIt is i-th fired power generating unit in period t average output, SiFor the start-up cost of i-th fired power generating unit, Toff,i,tFor
The when hop count that i-th fired power generating unit is continuously shut down in period t；
Constraints
1) power-balance condition
In formula, PW,jContributed for period t Wind turbines；
2) fired power generating unit power output constrains
Pmin,i≤PG,i,t≤Pmax,i
3) fired power generating unit climbing rate constrains
-DRi≤PG,i,t-PG,i,t-1≤URi
4) the minimum operation of fired power generating unit and idle time constraint
In formula, Ton,i,tThe when hop count continuously opened for i-th fired power generating unit in period t；
5) spare capacity constrains
Spare capacity constraint is divided into positive rotation Reserve Constraint and negative rotation turns Reserve Constraint, and wherein positive rotation Reserve Constraint is wind
Electricity causes required positive rotation standby when contributing minimum：
In formula, L% is the spinning reserve rate for system loading,PW,t For the lower limit of t period wind power outputs；
Negative rotation turns to cause required negative spinning reserve when Reserve Constraint is wind power output maximum：
In formula,For the upper limit of t period wind power outputs；
6) user satisfaction constrains
The satisfaction of jth type load power mode is ηj
In formula, ηj,minFor the minimum value of the satisfaction of jth type load power mode.
4th, the electricity pricing side according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type
Method, it is characterised in that the model solution method described in step f：
The polymorphic type flexible load to be generated electricity with wind-fire is coordinated into Controlling model and is converted into a dual-layer optimization problem, outside
Layer optimization problem be four type load time-of-use tariffs determination, internal layer optimization problem for fired power generating unit start and stop state determination and
Start the output situation of unit；The time-of-use tariffs of the type load of random initializtion four, and ask on this basis under the time-of-use tariffs
The power consumption of four type loads each period, the power consumption of superposition four type loads each period obtain the electricity consumption of total load each period
Amount, then solved using the output of start and stop state of the differential evolution particle cluster algorithm to fired power generating unit and startup unit,
Optimal cost of electricity-generating in the case of the time-of-use tariffs is obtained, recycles differential evolution particle cluster algorithm repeatedly to the peak of four type loads
Paddy electricity valency carries out optimizing, takes the minimum four corresponding type load time-of-use tariffs of cost of electricity-generating under all situations as final electricity price
Scheme.
Claims (4)
1. the electricity pricing method with wind-fire generating and the power network of flexible load containing polymorphic type, it is characterised in that this method includes
The following steps：
A, the fired power generating unit number N of power network, and the parameter of i-th unit are determined：I-th fired power generating unit cost of electricity-generating characteristic coefficient
For ai、biAnd ci, the thermal starting of i-th unit and cold start-up expense are respectively Shot,iAnd Scold,i, the minimum start of i-th unit
Time and minimum downtime are respectively Tminrun,iAnd Tminstop,i, the cold start-up time T of i-th unitcold,i, i-th thermoelectricity
The minimum output power P of machinemin,iWith peak power output Pmax,i, the descending climbing rate DR of i-th thermal motoriWith upward slope climbing rate
URi, wherein i=1,2 ..., N；
B, the sunrise force curve of wind-powered electricity generation is determined；
C, load is divided into the P of industrial load oneload,1, the P of industrial load twoload,2, Commercial Load Pload,3With resident load Pload,4
Four classes, determine the original electricity price p of jth type load0, j, it is determined that under original electricity price state jth type load t hours power consumption
L0(t),j, wherein j=1,2,3,4, t=1,2 ..., 24；
D, using Price elasticity matrix, model of the user to Respondence to the Price of Electric Power behavior is established；
E, with the minimum object function of fired power generating unit cost of electricity-generating, and power-balance constraint, fired power generating unit power output are provided about
Beam, the constraint of fired power generating unit climbing rate, the minimum operation of fired power generating unit and idle time constraint, fired power generating unit spare capacity are constrained and used
Family satisfaction constraint, establish the polymorphic type flexible load to be generated electricity with wind-fire and coordinate Controlling model；
F, using the above-mentioned model of differential evolution PSO Algorithm, the time-of-use tariffs of four kinds of flexible loads are obtained.
2. the electricity pricing method according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type, its
It is characterised by, the user described in step d is to the model of Respondence to the Price of Electric Power behavior：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>24</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>24</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>L</mi>
<mrow>
<mn>0</mn>
<mrow>
<mo>(</mo>
<mn>24</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mi>E</mi>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mn>24</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>p</mi>
<mrow>
<mn>0</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mfrac>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
In formula, p1(t),jThe electricity price reformulated for jth type load t hours, L1(t),jFor p1(t),jThe lower jth type load t of excitation
The power consumption of hour, t=1,2 ..., 24, j=1,2,3,4, wherein E is Price elasticity matrix：
<mrow>
<mi>E</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>1</mn>
<mo>,</mo>
<mn>24</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>2</mn>
<mo>,</mo>
<mn>24</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>24</mn>
<mo>,</mo>
<mn>1</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>24</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
<mtd>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mn>24</mn>
<mo>,</mo>
<mn>24</mn>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>.</mo>
</mrow>
3. the electricity pricing method according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type, its
It is characterised by, the foundation described in step e coordinates Controlling model with the polymorphic type flexible load that wind-fire generates electricity：
Object function
<mrow>
<mi>min</mi>
<mi> </mi>
<mi>F</mi>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>{</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mo>&lsqb;</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<mi>f</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&rsqb;</mo>
<mo>}</mo>
</mrow>
f(PG,i,t)=ai·(PG,i,t)2+bi·PG,i,t+ci
<mrow>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<msub>
<mi>S</mi>
<mrow>
<mi>h</mi>
<mi>o</mi>
<mi>t</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mrow>
<mo>(</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>min</mi>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
<mi>p</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>o</mi>
<mi>f</mi>
<mi>f</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>min</mi>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
<mi>p</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mi>S</mi>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
</mtd>
<mtd>
<mrow>
<mo>(</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>o</mi>
<mi>f</mi>
<mi>f</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>></mo>
<msub>
<mi>T</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
<mi>p</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>l</mi>
<mi>d</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
In formula, ui,tIt is i-th fired power generating unit in period t start and stop state, f (PG,i,t) it is i-th fired power generating unit fuel cost,
PG,i,tIt is i-th fired power generating unit in period t average output, SiFor the start-up cost of i-th fired power generating unit, Toff,i,tFor i-th
The when hop count that platform fired power generating unit is continuously shut down in period t；
Constraints
1) power-balance condition
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>G</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>W</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>4</mn>
</munderover>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
In formula, PW,jContributed for period t Wind turbines；
2) fired power generating unit power output constrains
Pmin,i≤PG,i,t≤Pmax,i
3) fired power generating unit climbing rate constrains
-DRi≤PG,i,t-PG,i,t-1≤URi
4) the minimum operation of fired power generating unit and idle time constraint
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>o</mi>
<mi>n</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>min</mi>
<mi>r</mi>
<mi>u</mi>
<mi>n</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>T</mi>
<mrow>
<mi>o</mi>
<mi>f</mi>
<mi>f</mi>
<mo>,</mo>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>T</mi>
<mrow>
<mi>min</mi>
<mi>s</mi>
<mi>t</mi>
<mi>o</mi>
<mi>p</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
In formula, Ton,i,tThe when hop count continuously opened for i-th fired power generating unit in period t；
5) spare capacity constrains
Spare capacity constraint is divided into positive rotation Reserve Constraint and negative rotation turns Reserve Constraint, and wherein positive rotation Reserve Constraint goes out for wind-powered electricity generation
Cause required positive rotation standby during power minimum：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<munder>
<msub>
<mi>P</mi>
<mrow>
<mi>W</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&OverBar;</mo>
</munder>
<mo>&GreaterEqual;</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<mi>L</mi>
<mi>%</mi>
<mo>)</mo>
</mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
In formula, L% is the spinning reserve rate for system loading,PW,t For the lower limit of t period wind power outputs；
Negative rotation turns to cause required negative spinning reserve when Reserve Constraint is wind power output maximum：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mi>u</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<mover>
<msub>
<mi>P</mi>
<mrow>
<mi>W</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&OverBar;</mo>
</mover>
<mo>&le;</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<mi>L</mi>
<mi>%</mi>
<mo>)</mo>
</mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msub>
<mi>P</mi>
<mrow>
<mi>l</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
In formula,For the upper limit of t period wind power outputs；
6) user satisfaction constrains
The satisfaction of jth type load power mode is ηj
<mrow>
<msub>
<mi>&eta;</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
<mo>|</mo>
<msub>
<mi>L</mi>
<mrow>
<mn>1</mn>
<mrow>
<mo>(</mo>
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</mrow>
</msub>
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<mi>L</mi>
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<mn>0</mn>
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</mrow>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<mo>|</mo>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
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<mn>1</mn>
</mrow>
<mn>24</mn>
</munderover>
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<mo>&GreaterEqual;</mo>
<msub>
<mi>&eta;</mi>
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In formula, ηj,minFor the minimum value of the satisfaction of jth type load power mode.
4. the electricity pricing method according to claim 1 with wind-fire generating and the power network of flexible load containing polymorphic type, its
It is characterised by, the model solution method described in step f：
The polymorphic type flexible load to be generated electricity with wind-fire is coordinated into Controlling model and is converted into a dual-layer optimization problem, outer layer is excellent
Change problem is the determination of four type load time-of-use tariffs, and internal layer optimization problem is the determination and startup of the start and stop state of fired power generating unit
The output situation of unit；The time-of-use tariffs of the type load of random initializtion four, and four classes under the time-of-use tariffs are asked on this basis
The power consumption of load each period, the power consumption of superposition four type loads each period obtain the power consumption of total load each period,
Then solved, obtained using the output of start and stop state of the differential evolution particle cluster algorithm to fired power generating unit and startup unit
Optimal cost of electricity-generating in the case of the time-of-use tariffs, differential evolution particle cluster algorithm is recycled repeatedly to the peak-trough electricity of four type loads
Valency carries out optimizing, takes the minimum four corresponding type load time-of-use tariffs of cost of electricity-generating under all situations as final electricity price side
Case.
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